A guide to accessing LLMs beyond standard metrics
At a time when both the number of artificial intelligence (AI) models and their capabilities are expanding rapidly, enterprises face an increasingly complex challenge: how to effectively evaluate and select the right large language models (LLMs) for their needs.
With the recent release of Meta’s Llama 3.2 and the proliferation of models like Google’s Gemma and Microsoft’s Phi, the landscape has become more diverse—and more complicated—than ever before. As organizations seek to leverage these tools, they must navigate a maze of considerations to find the solutions that best fit their unique requirements.Beyond traditional metrics
Publicly available metrics and rankings often fail to reflect a model’s effectiveness in real-world applications, particularly for enterprises seeking to capitalize on deep knowledge locked within their repositories of unstructured data. Traditional evaluation metrics, while scientifically rigorous, can be misleading or irrelevant for business use cases.
Consider Perplexity, a common metric that measures how well a model predicts sample text. Despite its widespread use in academic settings, Perplexity often correlates poorly with actual usefulness in business scenarios, where the true value lies in a model’s ability to understand, contextualize and surface actionable insights from complex, domain-specific content.
Enterprises need models that can navigate industry jargon, understand nuanced relationships between concepts, and extract meaningful patterns from their unique data landscape—capabilities that conventional metrics fail to capture. A model might achieve excellent Perplexity scores while failing to generate practical, business-appropriate responses.
Similarly, BLEU (Bilingual Evaluation Understudy) scores, originally developed for machine translation, are sometimes used to evaluate language models‘ outputs against reference texts. However, in business contexts where creativity and problem-solving are valued, adhering strictly to reference texts may be counterproductive. A customer service chatbot that can only respond with pre-approved scripts (which would score well on BLEU) might perform poorly in real customer interactions where flexibility and understanding context are crucial.The data quality dilemma
Another challenge of model evaluation stems from training data sources. Most open source models are heavily trained on synthetic data, often generated by advanced models like GPT-4. While this approach enables rapid development and iteration, it presents several potential issues. Synthetic data may not fully capture the complexities of real-world scenarios, and its generic nature often fails to align with specialized business needs.
